pgvector Made Simple: A Practical Guide to Vector Search in PostgreSQL
Vector search is no longer optional for modern applications. From semantic search and recommendations to AI-powered assistants and retrieval-augmented generation, developers are expected to work confidently with embeddings and similarity search. Yet most guides either oversimplify the topic or jump straight into theory without showing how things actually work in practice.
This book takes a different approach.
pgvector Made Simple is a hands-on, developer-focused guide to building real vector search systems using PostgreSQL and pgvector. It teaches you how vector databases behave in production, how to design for accuracy and performance, and how to avoid the common mistakes that lead to slow queries and poor results.
You will start by understanding what vector embeddings are and how pgvector stores and compares them inside PostgreSQL. From there, the book walks you through similarity search, indexing strategies, duplicate detection, and performance tuning using clear explanations and runnable examples. Every concept is grounded in real database behavior, not abstractions.
As you progress, you will learn how to scale pgvector systems safely, manage growing datasets, rebuild and maintain indexes, monitor performance, and make informed trade-offs between accuracy, speed, and storage. The book also shows how pgvector fits into modern AI and LLM workflows, helping you decide when pgvector alone is sufficient and when it should be combined with other tools.
This is not a theoretical overview or a marketing introduction. It is a practical guide written for developers who want to build reliable, production-ready vector search systems with confidence.
Who this book is for
Software engineers building search, recommendation, or AI-powered features
Backend developers using PostgreSQL in real applications
Data engineers exploring vector search without adopting an entirely new database
AI practitioners integrating embeddings into existing systems
What makes this book different
Clear, narrative explanations with no unnecessary theory
Fully runnable examples designed to teach through real use cases
Practical guidance on scaling, performance, and production readiness
A PostgreSQL-first approach that fits into existing stacks
If you want to understand pgvector deeply, avoid common pitfalls, and build vector search systems that actually work in production, this book will guide you step by step.
Build with confidence. Query with precision. Scale without guesswork.
